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Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems

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Abstract

This study investigates how consumers assess the quality of two types of recommender systems-collaborative filtering and content-based—in the context of e-commerce by using a modified version of the unified theory of acceptance and use of technology (UTAUT) model. Specifically, the concept of trust in the technological artifact is adapted to examine the intention to use recommender systems. Additionally, this study also considers hedonic and utilitarian product characteristics with the goal of presenting a comprehensive picture on recommender systems literature. This study utilized a 2 × 2 crossover within-subjects experimental design involving a total of 80 participants, who all evaluated each recommender system. The results suggest that the type of recommender system significantly moderates many relationships of the determinants of customer behavioral intent on behavioral intention to use recommender systems. Surprisingly, the type of product does not moderate any relationship on behavioral intention. This study holds importance in explaining the factors contributing to the use of recommender systems and understanding the relative influence of the two types of recommender systems on customer behavioral intention to use recommender systems. The finding also sheds light for designers on how to provide more effective recommender systems.

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Notes

  1. Content-based recommender systems do use other users’ information to identify and classify profiles and likely preferences for products. For example, Netflix would use their vast database about users and preferences to recommend movies to clients. This is not explicitly social, however, because the algorithms for making these associations are not transparent to the user.

  2. These eight models include the Technology Acceptance Model (TAM) (Davis 1989), the Theory of Reasoned Action (TRA) (Fishbein and Ajzen 1975), the Theory of Planned Behavior (TPB) (Ajzen 1991), the Combined TAM and TPB (C-TAM-TPB) (Taylor and Todd 1995), the Innovation Diffusion Theory (IDT) (Moore and Benbasat 1991), the Social Cognitive Theory (SCT) (Compeau and Higgins 1995), the Motivational Model (MM) (Davis et al. 1992), and the Model of PC Utilization (MPCU) (Thompson et al. 1991).

  3. One time-varying covariate of recommender type familiarity and one time-invariant covariate of age were also included in the analysis. These two variables were not part of the primary analysis and were only included to account for possible differences in behavioral intent due to subject age and/or differing previous experience with the technologies.

  4. The use of the STEST equations involved constructing tests for each treatment effect across models. For example, given that the treatment conditions for the four models were M1: collaborative hedonic, M2: collaborative utilitarian, M3: content-based hedonic, M4: content-based utilitarian, a test of the moderating effect of type of recommender system on PE would be… stest m1.PE1 + m2.PE2 − m3.PE3 − m4.PE4 = 0. If this test was rejected, then there would be evidence on an interaction of recommender type and PE on BI.

References

  • Adomavicius G, Tuzhilin A (2005) Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

    Article  Google Scholar 

  • Aggarwal P, Vaidyanathan R (2003) The perceived effectiveness of virtual shopping agents for search vs. experience goods. Adv Consum Res 30:347–348

    Google Scholar 

  • Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211

    Article  Google Scholar 

  • Anderson NH, Barrios AA (1961) Primacy-recency in impression formation. J Abnorm Soc Psychol 63(2):346–350

    Article  Google Scholar 

  • Ansari A, Essegaier S, Kohli R (2000) Internet recommendation systems. Mark Res 37(3):363–375

    Article  Google Scholar 

  • Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Manage Sci 57(8):1485–1509

    Article  Google Scholar 

  • Bandura A (1977) Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 84:191–215

    Article  Google Scholar 

  • Bearden WO, Etzel MJ (1982) Reference group influence on product and brand purchase decisions. Consum Res 9(2):183–194

    Article  Google Scholar 

  • Bettman JR (1979) An information processing theory of consumer choice. Addison-Wesley, Reading

    Google Scholar 

  • Brown S, Dennis A, Venkatesh V (2010) Predicting collaborative technology use: integrating technology adoption and collaboration research. J Manag Inf Syst 27(2):9–53

    Article  Google Scholar 

  • Cheung W, Chang MK, Lai VS (2000) Prediction of internet and world wide web usage at work: a test of an extended triandis model. Decis Support Syst 30(1):83–100

    Article  Google Scholar 

  • Childersa TL, Carrb CL, Peckc J, Carson S (2001) Hedonic and utilitarian motivations for online retail shopping behavior. J Retail 77(4):511–535

    Article  Google Scholar 

  • Choi J, Lee HJ, Kim YC (2011) The influence of social presence on customer intention to reuse online recommender systems: the roles of personalization and product type. Int J Electron Commer 16(1):129–154

    Article  Google Scholar 

  • Cooley R, Tan PN, Srivastava J (1999) WebSift: the web site information filter system. In: Paper presented at the the 1999 KDD workshop on web mining, San Diego, CA

  • Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340

    Article  Google Scholar 

  • Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 37(1):36–48

    Google Scholar 

  • Davis FD, Bagozzi RP, Warshaw PR (1992) Extrinsic and intrinsic motivation to use computers in the workplace1. J Appl Soc Psychol 22(14):1111–1132

    Article  Google Scholar 

  • Degemmis M, Lops P, Semeraro G, Costabile M, Guida S, Licchelli O (2004) Improving collaborative recommender systems by means of user profiles. In: Karat C-M, Blom JO, Karat J (eds) Designing personalized user experiences in eCommerce. Kluwer Academic Publishers, The Netherlands

    Google Scholar 

  • Dhar R, Wertenbroch K (2000) Consumer choice between hedonic and utilitarian goods. J Mark Res 37(1):60–71

    Article  Google Scholar 

  • Dickinger A, Arami M, Meyer D (2008) The role of perceived enjoyment and social norm in the adoption of technology with network externalities. Eur J Inf Syst 17(1):4–11

    Article  Google Scholar 

  • Feick L, Higie RA (1992) The effects of preference heterogeneity and source characteristics on ad processing and judgments about endorsers. J Advert 21:9–24

    Article  Google Scholar 

  • Fishbein M, Ajzen I (1975) Belief, attitude, intention, and behavior: an introduction to theory and research. Addison-Wesley, Reading

    Google Scholar 

  • Fornell C, Larcker DF (1981) Structural equation models with unobservable variables and measurement error: algebra and statistics. J Mark Res 18(3):382–388

    Article  Google Scholar 

  • Gallagher K, Parsons J, Foster KD (2001) A tale of two studies: replicating advertising effectiveness and content evaluation in print and on the web. J Advert Res 41(4):71–82

    Google Scholar 

  • Gefen D (2000) E-commerce: the role of familiarity and trust. Omega 28(6):725–737

    Article  Google Scholar 

  • Gefen D, Straub DW (2003) Managing user trust in B2C e-services. E-service J 2(2):7–24

    Article  Google Scholar 

  • Gefen D, Straub DW (2004) Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services. Omega 32(6):407–424

    Article  Google Scholar 

  • Gefen D, Karahanna E, Straub DW (2003) Trust and TAM in online shopping: an integrated model. MIS Q 27(1):51–90

    Google Scholar 

  • Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23(10):1498–1512

    Article  Google Scholar 

  • Goodhue D, Thompson R (1995) Task-technology fit and individual performance. MIS Q 19(2):213–236

    Article  Google Scholar 

  • Gutzman A (2000) Real time chat: what are you waiting for? http://www.ecommerce-guide.com/solutions/technology/article.php/322551

  • Hassanein K, Head M (2005) The impact of infusing social presence in the web interface: an investigation across product types. Int J Electron Commer 10(2):31–55

    Article  Google Scholar 

  • Helson H (1964) Adaptation-level theory: an experimental and systematic approach to behavior. Harper & Row, New York

    Google Scholar 

  • Heppner PP, Kivlighan DM, Wampold BE (2008) Research design in counseling Belmont. Thomson Brooks/Cole, CA

    Google Scholar 

  • Herlocker J, Konstan J, Riedl J (2000) Explaining collaborative filtering recommendations. In: Paper presented at the 2000 ACM conference on computer supported cooperative work, New York

  • Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

    Article  Google Scholar 

  • Hirschman EC, Holbrook MB (1982) Hedonic consumption: emerging concepts, methods and propositions. J Mark 46(3):92–101

    Article  Google Scholar 

  • Holbrook MB, Hirschman EC (1982) The experiential aspects of consumption: consumer fantasies, feelings, and fun. J Consum Res 9(2):132–140

    Article  Google Scholar 

  • Hong W, Chan FKY, Thong JYL, Chasalow LC, Dhillon G (2014) A framework and guidelines for context-specific theorizing in information systems research. Inf Syst Res 25(1):111–136

    Article  Google Scholar 

  • Hostler RE, Yoon YV, Guimaraes T (2005) Assessing the impact of internet agent on end users’ performance. Decis Support Syst 41(1):313–323

    Article  Google Scholar 

  • Igbaria M, Iivari J, Maragahh H (1995) Why do individuals use computer technology? A Finnish case study. Inf Manag 29(5):227–238

    Article  Google Scholar 

  • Jones N, Pu P (2007) User technology adoption issues in recommender systems. In: Proceedings of NAEC, ATSMA, pp 379–339

  • Jupiter Research (2003) Jupiter research reports that web site ‘personalization’ does not always provide positive results. Jupitermedia Corporation, Darien, Connecticut

    Google Scholar 

  • King MF, Balasubramanian SK (1994) The effects of expertise, end goal, and product type on adoption of preference formation strategy. Acad Mark Sci 22(2):146–159

    Article  Google Scholar 

  • Kobsa A, Koenenmann J, Pohl W (2001) Personalized hypermedia presentation techniques for improving online customer relationships. Knowl Eng Rev 16(2):111–155

    Article  Google Scholar 

  • Konstan JA, Riedl J (2012) Recommender systems: from algorithms to user experience. User Model User-Adap Inter 22:101–123

    Article  Google Scholar 

  • Kramer T (2007) The effect of measurement task transparency on preference construction and evaluations of personalized recommendations. J Mark Res 44(2):224–233

    Article  Google Scholar 

  • Krulwich B (1997) Lifestyle finder: intelligent user profiling using large-scale demographic data. AI Mag 18(2):37–54

    Google Scholar 

  • Kumar N, Benbasat I (2006) The influence of recommendations and consumer reviews on evaluations of websites. Inf Syst Res 17(4):425–429

    Article  Google Scholar 

  • Liang T-P, Lai H-J, Ku Y-C (2007) Personalized content recommendation and user satisfaction: theoretical synthesis and empirical findings. J Manag Inf Syst 23(3):45–70

    Article  Google Scholar 

  • Liberman H (1995) Letizia: an agent that assists web browsing. In: Paper presented at the international joint conference on artificial intelligence, Montreal, Canada

  • Luhmann N (1979) Trust and power. Wiley, London

    Google Scholar 

  • McKnight DH, Choudhury V, Kacmar C (2002) Developing and validating trust measures for e-commerce: an integrative typology. Inf Syst Res 13(3):334–359

    Article  Google Scholar 

  • McNee S, Riedl J, Konstan J (2006) Making recommendations better: an analytic model for human–recommender interaction. In: Paper presented at the 24th international conference human factors in computing systems, Montréal, Canada

  • Mobasher B, Cooley R, Srivastava J (2000) Automatic personalization based on web usage mining. Commun ACM 43(8):142–151

    Article  Google Scholar 

  • Montaner M, López B, de la Rosa JL (2003) A taxonomy of recommender agents on the internet. Artif Intell Rev 19(4):285–330

    Article  Google Scholar 

  • Moore GC, Benbasat I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res 2(3):192–222

    Article  Google Scholar 

  • Muller KE, Stewart PW (2006) Linear model theory: univariate, multivariate, and mixed models. Wiley, New York

    Book  Google Scholar 

  • Olshavsky RW (1985) Perceived quality in consumer decision-making: an integrated theoretical perspective. In: Olson JC (ed) Perceived uality: how consumers view stores and merchandise. D. C. Heath and Company, Lexington, pp 3–29

    Google Scholar 

  • Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web: methods and strategies of web personalization. Lecture notes in computer science. Springer, Berlin, pp 325–341

    Chapter  Google Scholar 

  • Pu P, Chen L (2006) Trust building with explanation interfaces. In: Paper presented at the proceedings of the 11th international conference on intelligent user interfaces

  • Pu P, Chen L, Hu R (2012) Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model User-Adap Inter 22:317–355

    Article  Google Scholar 

  • Rayport J, Jaworski B (2001) Introduction to e-Commerce. McGraw-Hill/Irwin, New York

    Google Scholar 

  • Resnick P, Varian HR (1997) Recommender Systems. Commun ACM 40(3):56–58

    Article  Google Scholar 

  • Ryu G, Park J, Feick L (2006) The role of product type and country-of-origin in decisions about choice of endorser ethnicity in advertising. Psychol Mark 23(6):487–513

    Article  Google Scholar 

  • Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web: methods and strategies of web personalization. Lecture notes in computer science. Springer, Berlin, pp 291–324

    Chapter  Google Scholar 

  • Senecal S, Nantel J (2004) The influence of online product recommendations on consumers’ online choices. J Retail 80(2):159–169

    Article  Google Scholar 

  • Sinha R, Swearingen K (2001) Comparing recommendations made by online systems and friends proceedings. In: Paper presented at the 2nd DELOS network of excellence workshop on personalisation and recommender systems in Digital Libraries, Dublin, Ireland

  • Sinha R, Swearingen K (2002) The role of transparency in recommender systems. In: Paper presented at the CHI 2002 extended abstracts on human factors in computing systems, Minneapolis, MN

  • Stafford MR, Stafford TF, Day E (2002) A contingency approach: the effects of spokesperson type and service type on service advertising perceptions. J Advert 31:17–35

    Article  Google Scholar 

  • Swearingen K, Sinha R (2002) Interaction design for recommender systems. In: Paper presented at the Designing Interactive System, London

  • Taylor S, Todd PA (1995) Assessing IT usage: the role of prior experience. MIS Q 19(4):561–570

    Article  Google Scholar 

  • Terveen L, Hill W (2001) Beyond recommender systems: helping people help each other. In: Carroll JM (ed) HCI in the New Millennium, vol 46. Addison-Wesley, New York, pp 487–509

    Google Scholar 

  • Thibaut JW, Kelley HH (1978) Interpersonal relations: a theory of interdependence. Wiley, New York

    Google Scholar 

  • van der Heijden H (2004) User acceptance of hedonic information systems. MIS Q 28(4):695–704

    Google Scholar 

  • Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci 46(2):186–204

    Article  Google Scholar 

  • Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478

    Google Scholar 

  • Vijayasarathy LR, Jones JM (2001) Do internet shopping aids make a difference? An empirical investigation. Electron Mark 11(1):75–83

    Article  Google Scholar 

  • Wang W, Benbasat I (2005) Trust in and adoption of online recommendation agents. J Assoc Inf Syst 6(3):72–101

    Google Scholar 

  • Wang W, Benbasat I (2007) Recommendation agents for electronic commerce: effects of explanation facilities on trusting beliefs. J Manag Inf Syst 23(4):217–246

    Article  Google Scholar 

  • Xiao B, Benbasat I (2007) E-commerce product recommendation agents: use, characteristics, and impact. MIS Q 31(1):137–209

    Google Scholar 

  • Zhang TC, Agarwal R, Lucas HC Jr (2011) The value of IT-enabled retailer learning: personalized product recommendations and customer store loyalty in electronic markets. MIS Q 35(4):859–881

    Google Scholar 

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Author information

Authors and Affiliations

  1. N204 North Business Complex, Department of Accounting and Information Systems, Michigan State University, East Lansing, MI, 48824, USA

    Yen-Yao Wang

  2. Department of Management Science and Information Systems, Oklahoma State University, Stillwater, OK, 74078, USA

    Andy Luse

  3. Department of Supply Chain and Information Systems, Iowa State University, Ames, IA, 50011, USA

    Anthony M. Townsend & Brian E. Mennecke

Authors
  1. Yen-Yao Wang
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  2. Andy Luse
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  3. Anthony M. Townsend
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  4. Brian E. Mennecke
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Corresponding author

Correspondence to Yen-Yao Wang.

Appendices

Appendix 1: Product manipulation

Nine product classes: cell phone, Laptop, Digital Camera, MP3 player, TV, Camcorder, Printer, and GPS

  1. 1.

    Hedonic = fun, enjoyable, for pleasure

    Utilitarian = work related, get a job done, accomplishes a task or useful goal

    Please rate these items based on whether you consider each to be closer to being utilitarian or hedonic (Seven point semantic differentials: 1 = Hedonic, 7 = Utilitarian)

  2. 2.

    Please rate these items based on whether you consider each to be closer to being exciting or dull (seven point semantic differentials: 1 = exciting, 7 = dull)

  3. 3.

    Please rate these items based on whether you consider each to be closer to being pleasant or unpleasant (seven point semantic differentials: 1 = pleasant, 7 = unpleasant)

  4. 4.

    Please rate these items based on whether you consider each to be closer to being interesting or boring (seven point semantic differentials: 1 = interesting, 7 = boring)

Appendix 2: Measurement items

2.1 Performance expectancy (PE)

PE1:

I would find the recommender system useful in searching and finding items

PE2:

Using the recommender system enables me to search and find items more quickly

PE3:

Using the recommender system increases my productivity in searching and finding items

PE4:

If I use the recommender system, I will increase my chances of getting better purchasing advice

2.2 Effort expectancy (EE)

EE1:

My interaction with the recommender system is clear and understandable

EE2:

It would be easy for me to become skillful at using the recommender system

EE3:

I would find the recommender system easy to use

EE4:

Learning to operate the recommender system is easy for me

2.3 Social influence (SI)

SI1:

Friends of mine would also find this system attractive

SI2:

People whose opinion I value would be in favor of using this system

SI3:

A business professor would recommend using this recommender system

SI4:

I believe that expert computer users would recommend this system

2.4 Trust

Trust1:

Even if the system were not monitored, I would trust the recommender system to recommend appropriate items

Trust2:

I trust the recommender system

Trust3:

I trust that the system makes reliable recommendations

2.5 Behavioral intentions (BI)

BI1:

I intend to use this type of recommender system in the next 6 months

BI2:

I predict I will use this type of recommendation system in the next 6 months

BI3:

I plan to use this type of recommendation system in the next 6 months

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Wang, YY., Luse, A., Townsend, A.M. et al. Understanding the moderating roles of types of recommender systems and products on customer behavioral intention to use recommender systems. Inf Syst E-Bus Manage 13, 769–799 (2015). https://doi.org/10.1007/s10257-014-0269-9

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